from PIL import ImageFile
from flask import Flask, request, jsonify
import face_recognition
import base64
from io import BytesIO
import joblib
import numpy as np

ImageFile.SAFEBLOCK = 2048 * 2048

app = Flask(__name__)
model_file_name = "saved_model.pkl"
clf = None
classes = None
dummy_data = [
    {
        "name": "Bayu",
        "address": "299 St Louis Road Oak Forest, IL 60452",
        "nik": "1000076456784631"
    },
    {
        "name": "Dio",
        "address": "22 Whitemarsh St. Mansfield, MA 02048",
        "nik": "1000024792887549"
    },
    {
        "name": "Hadi",
        "address": "643 Honey Creek Dr. Milledgeville, GA 31061",
        "nik": "1000038502830420"
    },
    {
        "name": "Kevin",
        "address": "881 Cooper Ave. Hummelstown, PA 17036",
        "nik": "1000045356476664"
    },
    {
        "name": "Matrix",
        "address": "580 Glenwood Dr. Garner, NC 27529",
        "nik": "1000023452134598"
    },
    {
        "name": "Surya",
        "address": "909 South St Paul Street Hopewell, VA 23860",
        "nik": "1000075656784734"
    },
]
ssl = None


@app.route('/predict', methods=['POST'])
def predict():
    result = []
    if "image" in request.json:
        im_b64 = request.json["image"]
    elif "image" in request.files:
        im_b64 = request.files["image"]
    elif "image" in request.form:
        im_b64 = request.form["image"]
    else:
        return {"error": "Error reading image"}
    im_bytes = base64.b64decode(im_b64)
    im_file = BytesIO(im_bytes)
    test_image = face_recognition.load_image_file(im_file)
    face_locations = face_recognition.face_locations(test_image)
    no = len(face_locations)
    for i in range(no):
        test_image_enc = face_recognition.face_encodings(test_image)[i]
        proba_list = clf.predict_proba([test_image_enc])
        i = np.argmax(proba_list)
        proba = list(*proba_list)[i]
        name = dummy_data[i]["name"]
        address = dummy_data[i]["address"]
        nik = dummy_data[i]["nik"]
        js = {
            "id": str(i),
            "name": name,
            "address": address,
            "nik": nik,
            "proba": proba
        }
        result.append(js)
    return jsonify(result)


if __name__ == '__main__':
    try:
        clf = joblib.load(model_file_name)
        classes = clf.classes_
        print('model loaded')

    except FileNotFoundError as e:
        print('No model here')
        exit(1)

    app.run(host='0.0.0.0', port=8349, debug=True, ssl_context=ssl)